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Predicting the Price of Molecules Using Their Predicted Synthetic Pathways.

Massina Abderrahmane¹, Hamza Tajmouati¹, Vinicius Barros Ribeiro da Silva¹

¹Iktos, 65 rue de Prony, 75017, Paris, France.

Molecular Informatics

|January 31, 2025

Summary

This summary is machine-generated.

Predicting the cost of novel molecules is crucial for drug discovery. This study introduces RetroPriceNet, a deep learning model that estimates compound prices using synthetic pathways and starting material costs, outperforming existing methods.

Keywords:

deep learning molecular price prediction retrosynthesis synthetic accessibility

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Area of Science:

Computational chemistry
Medicinal chemistry
Machine learning

Background:

Numerous metrics like QSAR models and docking scores aid in filtering virtual molecular libraries for synthesis.
Existing metrics do not account for the cost of novel virtual molecules, including starting material availability and price.
Accurate cost prediction can significantly improve and accelerate decision-making in drug development and cost-of-goods analysis.

Purpose of the Study:

To investigate the utility of predicted retrosynthetic pathways and starting material prices as features for predicting novel molecule costs.
To develop and present a deep learning model, RetroPriceNet, for accurate molecule price prediction.
To integrate synthetic feasibility and starting material economics into computational drug design.

Main Methods:

Utilized Computer Aided Synthetic Planning (CASP) to predict retrosynthetic pathways for virtual molecules.
Developed a deep learning model, RetroPriceNet, leveraging predicted synthetic routes and starting material prices.
Trained and evaluated the model on a dataset of molecules with known synthetic pathways and associated costs.

Main Results:

RetroPriceNet demonstrated superior performance in predicting molecule prices compared to the state-of-the-art model on a holdout test set.
The model effectively incorporates synthetic pathway information and starting material costs into its price predictions.
The developed approach provides a novel metric for assessing the economic viability of synthesizing virtual compounds.

Conclusions:

Predicted retrosynthetic pathways and starting material prices are valuable features for estimating novel molecule costs.
RetroPriceNet offers a powerful tool for accelerating decision-making in drug discovery by providing accurate cost predictions.
This work highlights the potential of integrating computational synthesis planning with economic factors in molecular design.